Towards Sustainable Energy-Efficient Communities Based on a Scheduling Algorithm

. 2019 Sep 14;19(18):3973. doi: 10.3390/s19183973

Algorithm 6 Optimisation based on PSO algorithm

1:
Initialise population of particles with random values positions in the search space $\vec{x}$ ∼U $[\vec{t_{b e g}}, \vec{t_{e n d}}]$
2:
Set each particle best known position to its initial position $\vec{p}$ ← $\vec{x}$
3:
Initialise each particle velocity to random values $\vec{v}$ ∼U[ $\vec{- d}$ , $\vec{d}$ ] where $\vec{d}$ = $\vec{b e g} - - \vec{e n d}$
4:
Initialise the best known position $\vec{g}$ to the $\vec{x}$ where $f (\vec{x}$ ) is lowest
5:
while Termination condition not reached do
6:
for Each particle i do
7:
if $i > 1$ then
8:
Choose two random numbers $ϕ_{p}$ , $ϕ_{g}$
9:
Adapt velocity $\vec{v}$ ← $ω$ $\vec{v}$ + $c_{p}$ $ϕ_{p}$ ( $\vec{p}$ – $\vec{x}$ ) + $c_{g}$ $ϕ_{g}$ ( $\vec{g}$ – $\vec{x}$ )
10:
Bound $\vec{v}$ for all dimensions i ( $\vec{v}$ , - $\vec{d}$ , $\vec{d}$ )
11:
Update the position of the particle $\vec{x}$ ← $\vec{x}$ + $\vec{v}$
12:
Bound population $x_{i}$ for all dimensions i ( $\vec{x}$ , $\vec{t_{b e g}}, \vec{t_{e n d}}$ )
13:
end if
14:
if $f (\vec{x}) < f (\vec{p})$ then
15:
update the particle’s best known position $\vec{p} \leftarrow \vec{x}$
16:
end if
17:
if $f (\vec{x}) < f (\vec{g})$ then
18:
update the particle’s best known position $\vec{g} \leftarrow \vec{x}$
19:
end if
20:
end for
21:
$\vec{g}$ holds the best found position in search space
22:
end while